• 전기전자학회논문지
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• 제5권1호
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• pp.100-110
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• 2001

본 논문에서는 공유 메모리형 ATM 스위치 설계에 있어서 스위치 자원의 이용률 향상과 서비스 품질 기능 지원을 위한 버퍼관리방안을 고찰하고 여러 기법들의 성능을 비교 분석하였다. 정적 임계법(ST)와 푸시 아웃(PO) 그리고, 동적 임계법(DT)의 성능을 시뮬레이션을 통하여 비교 분석하였고, 특히 동적임계법이 트래픽 부하 및 버스티니스 (Burstyness), 복수개 출력포트간 부하의 불균형성 (Non-uniformity)등의 트래픽 특성 변화에 대해 푸시아웃 (Pushout)에 가까운 견고성 (Robustness)을 가짐을 보였다. 또한, 서비스 품질 기능 구현에 있어서 연결승인제어 (CAC)로부터 구한 트래적 기술자를 이용하여 각 셀 스트림의 서비스 요구조건에 맞도록 메모리 공간을 할당하는 가상분할법 (VP: Virtual partitioning)과 동적분할법 (DP: Dynamic partitioning)등의 버퍼관리 메커니즘을 고찰하였다. 이 기법들을 사용할 경우 규정된 트래픽 (Regulated traffic)과 규정되지 않은 (Unregulated) best-effort 트래픽의 공존이 가능하며 규정되지 않은 트래픽이 존재하더라도 규정된 트래픽이 연결승인제어에 의해 계산된 셀 유실률을 보장받게 됨을 컴퓨터 시뮬레이션을 통하여 보였으며, 특히 과부하 상태에서 DP가 VP에 비해 서비스품질 지원 기능 면에서 우수함을 보였다.

• 전기학회논문지
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• 제58권2호
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• 한국정보전자통신기술학회논문지
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• 제5권4호
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• pp.224-231
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• 2012

본 논문에서는 비선형 공정을 퍼지 모델링하기 위해 FCM 클러스터링 알고리즘을 기반으로 하는 퍼지 추론 시스템을 소개한다. 비선형 공정에 대한 퍼지 규칙의 생성은 일반적으로 차원이 증가할수록 규칙의 수가 지수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해, FCM 클러스터링 알고리즘을 이용하여 입력 공간을 분산 형태로 분할함으로써 퍼지 모델의 규칙을 생성한다. 퍼지 규칙의 전반부 파라미터는 FCM 클러스터링 알고리즘에 의한 소속행렬로 결정된다. 퍼지 규칙의 후반부는 다항식 함수의 형태로 표현되며, 각 규칙의 후반부 파라미터들은 표준 최소자승법에 의해 동정된다. 마지막으로, 비선형 공정의 특성 및 성능을 평가하기 위하여 비선형 공정으로는 널리 이용되는 데이터를 이용한다.

• Structural Engineering and Mechanics
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• 제43권2호
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• pp.179-197
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• 2012

This study presents the findings of the structural health monitoring and the real time system identification of one of the first large scale building instrumentations in Turkey for earthquake safety. Within this context, a thorough review of steps in the instrumentation, monitoring is presented and seismic performance evaluation of structures using both nonlinear pushover and nonlinear dynamic time history analysis is carried out. The sensor locations are determined using the optimal sensor placement techniques used in NASA for on orbit modal identification of large space structures. System identification is carried out via the stochastic subspace technique. The results of the study show that under ambient vibrations, stocky buildings can be substantially stiffer than what is predicted by the finite element models due to the presence of a large number of partitioning walls. However, in a severe earthquake, it will not be safe to rely on this resistance due to the fact that once the partitioning walls crack, the bare frame contributes to the lateral stiffness of the building alone. Consequently, the periods obtained from system identification will be closer to those obtained from the FE analysis. A technique to control the validity of the proportional damping assumption is employed that checks the presence of phase difference in displacements of different stories obtained from band pass filtered records and it is confirmed that the "proportional damping assumption" is valid for this structure. Two different techniques are implemented for identifying the influence of the soil structure interaction. The first technique uses the transfer function between the roof and the basement in both directions. The second technique uses a pre-whitening filter on the data obtained from both the basement and the roof. Subsequently the impulse response function is computed from the scaled cross correlation between the input and the output. The overall results showed that the structure will satisfy the life safety performance level in a future earthquake but some soil structure interaction effects should be expected in the North South direction.

• 한국정보통신학회논문지
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• 제15권10호
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• pp.2072-2078
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• 2011

태양광을 이용한 태양광 발전시스템은 태양을 정면으로 바라 볼 때 가장 큰 효율을 얻을 수 있다. 즉, 태양의 위치에 대한 집광판(PV; Photovoltic)의 법선벡터를 일치시켜야 가장 높은 효율을 얻게 된다. 본 논문에서는 시간의 변화에 따라 태양의 이동경로를 추적할 수 있도록 태양의 그림자를 판독할 수 있는 8개의 CdS 센서 모듈을 통하여 태양의 위치를 판독하여 태양의 위치를 추적할 수 있는 시스템을 제시하고자 한다. 태양광 추적시스템의 퍼지제어기(fuzzy controller)는 퍼지 입력공간에 대한 격자형 퍼지분할(grid-type fuzzy partition) 영역으로 분할한 후 퍼지규칙(fuzzy rule)을 수립하여 시스템을 제어하도록 설계하였다. 본 논문에서는 태양광 추적을 위한 간단한 모형의 2축 제어시스템을 구성하였으며, CdS 모듈의 좌표축과 집광판의 좌표축을 일치시키도록 구성하였다. 이러한 시스템은 고정된 장소 및 선박과 같은 이동하는 환경에 효과적으로 태양광을 추적할 수 있는 지능형 퍼지제어기의 적용가능 성을 제시하고자 한다.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• Structural Monitoring and Maintenance
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• 제6권4호
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• pp.317-346
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• 2019

Performance assessment of pavements proves useful, in terms of handling the ride quality, controlling the travel time of vehicles and adequate maintenance of pavements. Roughness profiles provide a good measure of the deteriorating condition of the pavement. For the accurate estimates of pavement roughness from dynamic vehicle responses, vehicle parameters should be known accurately. Information on vehicle parameters is uncertain, due to the wear and tear over time. Hence, condition monitoring of pavement requires the identification of pavement roughness along with vehicle parameters. The present study proposes a scheme which estimates the roughness profile of the pavement with the use of accurate estimates of vehicle parameters computed in parallel. Pavement model used in this study is a two-layer Euler-Bernoulli beam resting on a nonlinear Pasternak foundation. The asphalt topping of the pavement in the top layer is modeled as viscoelastic, and the base course bottom layer is modeled as elastic. The viscoelastic response of the top layer is modeled with the help of the Burgers model. The vehicle model considered in this study is a half car model, fitted with accelerometers at specified points. The identification of the coupled system of vehicle-pavement interaction employs a coupled scheme of an unbiased minimum variance estimator and an optimization scheme. The partitioning of observed noisy quantities to be used in the two schemes is investigated in detail before the analysis. The unbiased minimum variance estimator (MVE) make use of a linear state-space formulation including roughness, to overcome the linearization difficulties as in conventional nonlinear filters. MVE gives estimates for the unknown input and fed into the optimization scheme to yield estimates of vehicle parameters. The issue of ill-posedness of the problem is dealt with by introducing a regularization equivalent term in the objective function, specifically where a large number of parameters are to be estimated. Effect of different objective functions is also studied. The outcome of this research is an overall measure of pavement condition.